John Ashi

RESEARCH, TEACHING, or OTHER INTERESTS

General Environmental Science, Artificial Intelligence

Scopus Publications

Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images
Oluibukun Gbenga Ajayi, John Ashi, Blessed Guda
Smart Agricultural Technology, 2023
For sustainability and efficiency in maintaining high crop yield and less chemically polluted agricultural lands, precise weed mapping is essential for the total implementation of site-specific weed management which currently stands as a major challenge in present day agriculture. In this research, the robustness of the training epochs of You Only Look Once (YOLO) v5s, a Convolutional Neural Network (CNN) model was evaluated for the development of an automatic crop and weeds classification using UAV images. The images were annotated using a bounding box and they were trained on google colaboratory over 100, 300, 500, 600, 700 and 1000 epochs. The model detected and categorized five different classes which are sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. To find the optimal performance on the test set, the model was trained across several epochs, and training was stopped when the test performance (classification accuracy, precision, and recall) began to drop. The obtained result shows that the performance of the classifier improved significantly as the range of training epochs tends to rise from 100 through to 600 epochs. Meanwhile, a slight decline was observed as the number of epoch was increased to 700 when the classification accuracy, the precision of weed and recall of 65, 43 and 43%, respectively, was recorded as against 67, 78 and 34% that was obtained as the classification accuracy, weed precision and recall, respectively, at 600 epochs. This decline continued even when the epoch was increased to 1000 where classification accuracy, weed precision and recall of 65%, 45% and 40%, respectively was obtained. The results showed that the training epoch of YOLOv5s significantly affects the model's robustness in automatic crop and weep classification and identified 600 as the epoch for optimal performance.
Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme
Oluibukun Gbenga Ajayi, John Ashi
Smart Agricultural Technology, 2023
Site-specific weed detection and management is a crucial approach for crop production management and herbicide contamination mitigation in precision agriculture. With the advent of unmanned aerial vehicles (UAVs) and advances in deep learning techniques, it has become possible to identify and classify weeds from crops at desired spatial and temporal resolution. In this research, a faster region based convolutional neural network was implemented for the automatic weed identification and classification using a mixed crop farmland as a case study. A DJI phantom 4 UAV was used to simultaneously collect about 254 image pairs of the study site. The images were annotated before transferring them into google colaboratory where they were trained over five epochs; 10,000, 20,000, 100,000, 200,000, and 242,000 with the aim of detecting the point when the model flattens out in the process of automatically identifying and classifying the weeds. The neural network identified and classified five classes which are; sugarcane, spinach, banana, pepper, and weeds. Finally, the accuracy of the automatic weed classification was evaluated with the aid of the recorded loss function and confusion matrix, and the result shows that the implemented model gave a classification accuracy of 52.5%, weed precision of 50%, weed recall of 7.7% and F1 score of 71.6% at 10,000 epochs, classification accuracy of 67.8%, weed precision of 67%, weed recall of 52.4% and a F1 score of 85.9% at 20,000 epochs, classification accuracy of 97.2%, weed precision of 96.2%, weed recall of 97.5% and a F1 score of 99% at 100,000 epochs, classification accuracy of 98.3%, weed precision of 98.1%, weed recall of 99.1% and a F1 score of 99.4% at 200,000 epochs, and classification accuracy of 97%, weed precision of 95%, weed recall of 99% and a F1 score of 99% at 242,000 epochs. It was observed that the model's performance improves significantly with increase in the number of epochs but got saturated at 242,000 epochs. The findings showed that the faster RCNN is robust for automatic weed identification and classification in a mixed crop farm.

RECENT SCHOLAR PUBLICATIONS

Monitoring Desertification and its Environmental Effects in Sokoto State using Geospatial Approach
B Mahmud
BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY 20 (1), 64-76 , 2025
2025.0
Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images
OG Ajayi, J Ashi, B Guda
Smart Agricultural Technology 5, 100231 , 2023
2023.0
Citations: 111
PERFORMANCE EVALUATION OF SELECTED DEEP LEARNING ALGORITHMS IN AUTOMATIC WEED DETECTION SYSTEM.
J Ashi
2023.0
Effect of varying training epochs of a faster region-based convolutional neural network on the accuracy of an automatic weed classification scheme
OG Ajayi, J Ashi
Smart Agricultural Technology 3, 100128 , 2023
2023.0
Citations: 107
Applicability of artificial neural network for automatic crop type classification on UAV-based images
OG Ajayi, YD Opaluwa, J Ashi, WM Zikirullahi
Environmental Technology and Science Journal , 2022
2022.0
Citations: 9
Smart Agricultural Technology
OG Ajayi, J Ashi, B Guda

MOST CITED SCHOLAR PUBLICATIONS

Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images
OG Ajayi, J Ashi, B Guda
Smart Agricultural Technology 5, 100231 , 2023
2023.0
Citations: 111
Effect of varying training epochs of a faster region-based convolutional neural network on the accuracy of an automatic weed classification scheme
OG Ajayi, J Ashi
Smart Agricultural Technology 3, 100128 , 2023
2023.0
Citations: 107
Applicability of artificial neural network for automatic crop type classification on UAV-based images
OG Ajayi, YD Opaluwa, J Ashi, WM Zikirullahi
Environmental Technology and Science Journal , 2022
2022.0
Citations: 9
Monitoring Desertification and its Environmental Effects in Sokoto State using Geospatial Approach
B Mahmud
BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY 20 (1), 64-76 , 2025
2025.0
PERFORMANCE EVALUATION OF SELECTED DEEP LEARNING ALGORITHMS IN AUTOMATIC WEED DETECTION SYSTEM.
J Ashi
2023.0
Smart Agricultural Technology
OG Ajayi, J Ashi, B Guda